Naslov
Handling Unknown and Imprecise Attribute Values in Propositional Rule Learning: A Feature-Based Approach

Autori
Gamberger, Dragan ; Lavrač, Nada ; Fuernkranz, Johannes

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
PRICAI 2008: Trends in Artificial Intelligence / Ho, Tu-Bao ; Zhou, Zhi-Hua - Berlin : Heidelberg : Springer, 2008, 636-645

ISBN
978-3-540-89196-3

Skup
10th Pacific Rim International Conference on Artificial Intelligence

Mjesto i datum
Hanoi, Vijetnam, 15.12.2008. - 19.12.2008

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Rule learnig; Features; Unknown attribute value; Imprecision of attribute values

Sažetak
Rule learning systems use features as the main building blocks for rules. A feature can be a simple attribute-value test or a test of the validity of a complex domain knowledge relationship. Most existing concept learning systems generate features in the rule construction process. However, the separation of feature generation and rule construction processes has several theoretical and practical advantages. In particular, the proposed transformation from the attribute to the feature space motivates a novel, theoretically justified procedure for handling of unknown attribute values. This approach suggests also a novel procedure for handling imprecision of numerical attributes. The possibility of controlling the expected imprecision of numerical attributes during the induction process is a novel machine learning concept which has a high application potential for solving real world problems.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA